import tensorflow as tf


# %%
def inference(images, batch_size, n_classes):
    '''Build the model
  Args:
      images: image batch, 4D tensor, tf.float32, [batch_size, width, height, channels]
  Returns:
      output tensor with the computed logits, float, [batch_size, n_classes]
  '''
    # conv1, shape = [kernel size, kernel size, channels, kernel numbers]

    with tf.variable_scope('conv1') as scope:
        weights = tf.get_variable('weights',
                                  shape=[5, 5, 1, 32],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[32],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding='SAME')
        pre_activation = tf.nn.bias_add(conv, biases)
        conv1 = tf.nn.relu(pre_activation, name=scope.name)

    # pool1 and norm1
    with tf.variable_scope('pooling1_lrn') as scope:
        pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
                               padding='SAME', name='pooling1')
        norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001 / 9.0,
                          beta=0.75, name='norm1')

    # conv2
    with tf.variable_scope('conv2') as scope:
        weights = tf.get_variable('weights',
                                  shape=[5, 5, 32, 32],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[32],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        conv = tf.nn.conv2d(norm1, weights, strides=[1, 1, 1, 1], padding='SAME')
        pre_activation = tf.nn.bias_add(conv, biases)
        conv2 = tf.nn.relu(pre_activation, name='conv2')

    # pool2 and norm2
    with tf.variable_scope('pooling2_lrn') as scope:
        norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001 / 9.0,
                          beta=0.75, name='norm2')
        pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
                               padding='SAME', name='pooling2')
    # with tf.variable_scope('conv3') as scope:
    #     weights = tf.get_variable('weights',
    #                               shape=[3, 3, 32, 64],
    #                               dtype=tf.float32,
    #                               initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))
    #     biases = tf.get_variable('biases',
    #                              shape=[64],
    #                              dtype=tf.float32,
    #                              initializer=tf.constant_initializer(0.1))
    #     conv = tf.nn.conv2d(pool2, weights, strides=[1, 1, 1, 1], padding='SAME')
    #     pre_activation = tf.nn.bias_add(conv, biases)
    #     conv3 = tf.nn.relu(pre_activation, name='conv3')
    #
    #     # pool2 and norm2
    # with tf.variable_scope('pooling3_lrn') as scope:
    #     norm3 = tf.nn.lrn(conv3, depth_radius=4, bias=1.0, alpha=0.001 / 9.0,
    #                       beta=0.75, name='norm2')
    #     pool3 = tf.nn.max_pool(norm3, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
    #                            padding='SAME', name='pooling3')

    # local3
    with tf.variable_scope('local3') as scope:
        reshape = tf.reshape(pool2, shape=[batch_size, -1])
        dim = reshape.get_shape()[1].value
        weights = tf.get_variable('weights',
                                  shape=[dim, 512],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[512],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)

        # local4
    with tf.variable_scope('local4') as scope:
        weights = tf.get_variable('weights',
                                  shape=[512, 512],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[512],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4')

    # softmax
    with tf.variable_scope('softmax') as scope:
        weights = tf.get_variable('weights',
                                  shape=[512, n_classes],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[n_classes],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear')
        softmax = tf.nn.softmax(softmax_linear,name='softmax')

    return softmax,pool2


# %%
def losses(logits, labels):
    '''Compute loss from logits and labels
  Args:
      logits: logits tensor, float, [batch_size, n_classes]
      labels: label tensor, tf.int32, [batch_size]

  Returns:
      loss tensor of float type
  '''
    with tf.variable_scope('loss') as scope:
        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits \
            (logits=logits, labels=labels, name='xentropy_per_example')
        loss = tf.reduce_mean(cross_entropy, name='loss')
        tf.summary.scalar(scope.name + '/loss', loss)
    return loss


# %%
def trainning(loss, learning_rate):
    '''Training ops, the Op returned by this function is what must be passed to
      'sess.run()' call to cause the model to train.

  Args:
      loss: loss tensor, from losses()

  Returns:
      train_op: The op for trainning
  '''
    with tf.name_scope('optimizer'):
        global_step = tf.Variable(0, name='global_step', trainable=False)
        learning_rate = tf.train.exponential_decay(learning_rate, global_step, decay_steps=1000, decay_rate=0.96,
                                                   staircase=True)
        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
        train_op = optimizer.minimize(loss, global_step=global_step)
    return train_op


# %%
def evaluation(logits, labels):
    """Evaluate the quality of the logits at predicting the label.
  Args:
    logits: Logits tensor, float - [batch_size, NUM_CLASSES].
    labels: Labels tensor, int32 - [batch_size], with values in the
      range [0, NUM_CLASSES).
  Returns:
    A scalar int32 tensor with the number of examples (out of batch_size)
    that were predicted correctly.
  """
    with tf.variable_scope('accuracy') as scope:
        correct = tf.nn.in_top_k(logits, labels, 1)
        correct = tf.cast(correct, tf.float16)
        accuracy = tf.reduce_mean(correct)
        tf.summary.scalar(scope.name + '/accuracy', accuracy)
    return accuracy

# %%
# test
def num_correct_prediction(logits, labels):
  """Evaluate the quality of the logits at predicting the label.
  Return:
      the number of correct predictions
  """
  labels = tf.cast(labels, tf.int64)
  correct = tf.equal(tf.argmax(logits, 1), labels)
  correct = tf.cast(correct, tf.int32)
  n_correct = tf.reduce_sum(correct)
  return n_correct